January 2020 - Dev - lists.ceph.io

by Patrick Donnelly

For developers submitting jobs using teuthology, we now have recommendations on what priority level to use: https://docs.ceph.com/docs/master/dev/developer_guide/#testing-priority -- Patrick Donnelly, Ph.D. He / Him / His Senior Software Engineer Red Hat Sunnyvale, CA GPG: 19F28A586F808C2402351B93C3301A3E258DD79D

1 week

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Windows port

by Lucian Petrut

Hi, We're happy to announce that a couple of weeks ago, we've submitted a few Github pull requests[1][2][3] adding initial Windows support. A big thank you to the people that have already reviewed the patches. To bring some context about the scope and current status of our work: we're mostly targeting the client side, allowing Windows hosts to consume rados, rbd and cephfs resources. We have Windows binaries capable of writing to rados pools[4]. We're using mingw to build the ceph components, mostly due to the fact that it requires the minimum amount of changes to cross compile ceph for Windows. However, we're soon going to switch to MSVC/Clang due to mingw limitations and long standing bugs[5][6]. Porting the unit tests is also something that we're currently working on. The next step will be implementing a virtual miniport driver so that RBD volumes can be exposed to Windows hosts and Hyper-V guests. We're hoping to leverage librbd as much as possible as part of a daemon that will communicate with the driver. We're also aiming at cephfs and considering using Dokan, which is FUSE compatible. Merging the open PRs would allow us to move forward, focusing on the drivers and avoiding rebase issues. Any help on that is greatly appreciated. Last but not least, I'd like to thank Suse, who's sponsoring this effort! Lucian Petrut Cloudbase Solutions [1] https://github.com/ceph/ceph/pull/31981 [2] https://github.com/ceph/ceph/pull/32027 [3] https://github.com/ceph/rocksdb/pull/42 [4] http://paste.openstack.org/raw/787534/ [5] https://sourceforge.net/p/mingw-w64/bugs/816/ [6] https://sourceforge.net/p/mingw-w64/bugs/527/

2 years, 10 months

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Send bucket policy to OPA server

by Seena Fallah

Hi all. In OPA integration from Ceph there is no integration for bucket policy. When user is setting bucket policy to his/her bucket the OPA server won't get who get's access to that bucket so after that if the request is coming from the user (that gets access to that bucket via bucket policy) to access that bucket (PUT, GET,...), OPA will reject that because of no data in database. I have create a pull request for this problem so if user creates a bucket policy for his/her bucket, the policy data will send to OPA server to be update on the database. I think the main idea of having OPA is to have all authorization in OPA and Ceph don't authorize any request by it self. Here is the pull request and I would be thankful to hear about your comments. https://github.com/ceph/ceph/pull/32294 Thanks.

3 years

7
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zfs2ceph and ideas for supporting migration to Ceph

by Neal Gompa

Hello all, I don't know how many of you folks are aware, but early last year, Datto (full disclosure, my current employer, though I'm sending this email pretty much on my own) released a tool called "zfs2ceph" as an open source project[1]. This project was the result of a week-long internal hackathon (SUSE folks may be familiar with this concept from their own "HackWeek" program[2]) that Datto held internally in December 2018. I was a member of that team, helping with research, setting up infra, and making demos for it. Anyway, I'm bringing it up here because I'd had some conversations with some folks individually who suggested that I bring it up here in the mailing list and to talk about some of the motivations and what I'd like to see in the future from Ceph on this. The main motivation here was to provide a seamless mechanism to transfer ZFS based datasets with the full chain of historical snapshots onto Ceph storage with as much fidelity as possible to allow a storage migration without requiring 2x-4x system resources. Datto is in the disaster recovery business, so working backups with full history are extremely valuable to Datto, its partners, and their customers. That's why the traditional path of just syncing the current state and letting the old stuff die off is not workable. At the scale of having literally thousands of servers with each server having hundreds of terabytes of ZFS storage (making up in aggregate to hundreds of petabytes of data), there's no feasible way to consider alternative storage options without having a way to transfer datasets from ZFS to Ceph so that we can cut over servers to being Ceph nodes with minimal downtime and near zero new server purchasing requirements (there's obviously a little bit of extra hardware needed to "seed" a Ceph cluster, but that's fine). The current zfs2ceph implementation handles zvol sends and transforms them into rbd v1 import streams. I don't recall exactly the reason why we don't use v2 anymore, but I think there was some gaps that made it so it wasn't usable for our case back then (we were using Ceph Luminous). I'm unsure if this is improved now, though it wouldn't surprise me if it has. However, zvols aren't enough for us. Most of our ZFS datasets are in the ZFS filesystem form, not the ZVol block device form. Unfortunately, there is no import equivalent for CephFS, which blocked an implemented of this capability[3]. I had filed a request about it on the issue tracker, but it was rejected on the basis of something was being worked on[4]. However, I haven't seen something exactly like what I need land in CephFS yet. The code is pretty simple, and I think it would be easy enough for it to be incorporated into Ceph itself. However, there's a greater question here. Is there interest from the Ceph developer community in developing and supporting strategies to migrate from legacy data stores to Ceph with as much fidelity as reasonably possible? Personally, I hope so. My hope is that this post generates some interesting conversation about how to make this a better supported capability within Ceph for block and filesystem data. :) Best regards, Neal [1]: https://github.com/datto/zfs2ceph [2]: https://hackweek.suse.com/ [3]: https://github.com/datto/zfs2ceph/issues/1 [4]: https://tracker.ceph.com/issues/40390 -- 真実はいつも一つ！/ Always, there's only one truth!

3 years

3
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Attempt to rethink log-based replication in Ceph on fast IO path

by Roman Penyaev

Hi Sage and all, I would like to share some of my scattered thoughts regarding IO performance improvements in Ceph. I touch on topics such as replicated log model, atomicity and full journaling on the objectstore side and how these constraints can be relaxed on IO path in order to squeeze maximum from a hard drive, at the same time still respecting the strong data consistency between replicas. Would be great to have a chance to discuss this in Seoul. Proposal in a few lines (for time saving) ----------------------------------------- 1. IO and management requests follow different paths: 1. Slow path for management requests (object creation/deletion, locks, etc) involves replicated log and primary-copy model, as it is right now. 2. Fast IO path (read, write requests) bypasses replicated log and makes clients responsible for sending data to each replica in a group (PG), I reference it as hybrid client-driven replication further in the text. 2. For the sake of simplicity client takes fast IO path only when group of replicas (PG) is in active and healthy state (active+client in terms of Ceph), otherwise client falls back to the slow path. Thus, client is not involved in the disaster recovery process (that's why hybrid). 3. Fast IO path (is taken only if group of replicas (PG) is in active and healthy state): 1. Reads of objects happen from different primaries in a group (PG), in order to spread the read load. 2. Writes are distributed by clients themselves to all replicas in a group (PG), avoiding one extra network hop. 4. Objectstore becomes journal-less and behaves similar to RAID 1, sacrificing atomicity and erasure coding (due to the "write hole" problem) for write performance, which becomes close to the write performance of a bare hard drive, but still keeping strong data consistency between replicas. !!!!! CAUTION: many letters !!!!! Introduction ------------ The most impressive IO performance numbers among products which provide data redundancy is certainly delivered by RAID and that is not a surprise: you just write a block to several disks in parallel, nothing more. Perfect. Then why Ceph can't achieve the same performance as RAID 1 on IO path, doing plain old write-in-parallel thing, but supplemented with scalability, CRUSH object placement and all other beloved things which make Ceph so powerful? The answer to this question lies in the replication design used by Ceph, namely log-based replication (PG log in Ceph terms), which is commonly used in storage and database systems and well described in many publications [1, 2, 3, 4]). Sequential replicated log indeed resolves issues related to atomicity, consistency, isolation and durability (this famous ACID buzzword acronym), especially in DBMS world where transactions are involved. But Ceph is a storage, not a database. Modern hard drives do not provide atomicity, strict writes ordering or durability. Hardware breaks all the rules and relax the constraints for the sake of performance, and software has to deal with it. Every modern filesystem can survive crashes (journals, COW, soft-updates, etc approaches are used), each application which cares about data consistency and can't rely on weak guarantees of POSIX file API (any DBMS for example) uses same methods (journals, COW, etc) to avoid inconsistency. Why then a storage solution, which works below a filesystem and on top of a hardware, should provide such strong ACID properties for IO? So what is a log-based replication which is a core of Ceph? Very simple: duplication (mirroring) of a log which consist of records describing storage operations. In order to duplicate something we have to be sure that an object which we duplicate has not been changed on the way, i.e. actually copy-paste does what it is supposed to do without nasty surprises. When we have a log of storage operations we need not only to deliver a recent record to a replica without corruption (and wrong order of operations in a log is a corruption) but also invoke this operation, which has been described in a log record. Not only that: new record in a log and operation, which is described by that record, should happen in one transaction, atomically, thus either we see a record and operation was performed, either no updates in a log and no operation consequences is visible. Having such a wonderful log (which obviously can survive a crash or even a nuclear strike) we can very easily answer The Most Important question for a high scalable, self-healing storage product: what has happened on other replicas while one of them took a nap (crashed). In other words we need such a complicated mechanism only for one thing: just to tell the exact data difference (e.g. in blocks of data and operations), what has been changed since a crash. Short quiz: can Сeph stay Ceph, but without any log-based replication? Yes, throw it away, no need to know the difference, just copy the whole bunch of data between replicas on any disaster recovery. Very slow on resyncing but fast on doing operations and simple. I'm, of course, exaggerating, but that is not so far from the truth. Talking about log-based replication I've never mentioned why it actually impacts the IO performance. Here is why: 1. Each update operation on a group of replicas (PG in terms of Ceph) has to be strictly ordered (log should stay in the same order on all replicas) i.e. no any other operation is allowed till an operation, which has been just started, reaches a hard drive with all cache flushes involved (who poked the code knows the notorious pglock, which protects the replication log from corruption and makes it be equal on all other replicas). 2. Severe restrictions on how actually the objectstore should be implemented: data and metadata should be fully journaled, because a log record of a replicated operation is stored along with the data in atomic manner. And what is wrong with journal in terms of performance? Nothing is wrong, it is just incredibly slow. (As a hint for the curious: enable "data=journal" option for ext4 filesystem, or "allow_cow" for xfs or just run any load which performs random writes on BTRFS (cow is always enabled), and yes, I promise, you feel the difference immediately). 3. Communication with a group of replicas (PG) happens only through a primary one, where a primary is responsible for keeping log in sync with other replicas. This primary-copy model [7] increases IO latency adding one extra network hop. All of the above about log-based replication in Ceph is about strong consistency, but not about performance. But can we have both? And what is actually a strong consistency? According to formal description "The protocol is said to support strong consistency if all accesses are seen by all parallel processes (or nodes, processors, etc.) in the same order (sequentially)" [6]. In simple words: one thread produces data to the same offset of a file, other threads read from the same offset, thus if strong consistency is respected readers never observe writes in a wrong order, e.g.: Writer Reader 1 Reader 2 Reader 3 -------------- -------------- -------------- --------------- write(file, "A"); b0 = read(file); b0 = read(file); b1 = read(file); write(file, "B"); b0 = read(file); b1 = read(file); b1 = read(file); In this example, readers are able to see a block of a file in the following states: Reader 1 Reader 2 Reader 3 --------- ---------- ---------- b0 == "A" b0 == "A" b0 == "B" b1 == "B" b1 == "A" b1 == "B" But what should never be observed and can be treated as a corruption is the following: b0 == "B" b1 == "A" Having that we can develop a simple rule: writes are not blocked and can come at any order, once a reader observes a change in a block, further reads to this block see the change. Most likely I describe here a wheel, which has some vivid name and was deeply buried in some publication in early 70th, but I do not care and call the rule as READ RECENT. The READ RECENT rule contains one splendid property: if read has never happened and order of writes is unknown, writes can be reordered regardless of their actual order. Practical example: there is a distributed storage where writes are unordered by nature; a full outage has happened and now replicas contain different blocks of data on the same offsets; read of the block has never happened after it was updated, thus we are able to bring replicas in sync choosing block from *any* replica. Marvelous. I will return to this highly important property when I start describing a new replication model. It turns out that strong consistency is not about strict requests ordering in a whole group of replicas (PG) it is only about order of reads against writes to the same block of data. That's it. So the question is left unanswered: can we have both strong consistency and high performance of IO? In order to answer this question, it is necessary to once again state the main requirements for the IO replication model, which remove the strict restrictions imposed by the log-based replication, positively affect performance and do not contradict strong consistency: 1. Get rid of extra hop on fast IO path by doing client-driven replication, at least when group of replicas (PG) is in active state and not doing any sort of disaster recovery. 2. Write requests are not strictly ordered to different offsets of an object and can be submitted to a hard drive in parallel. 3. Get rid of any journaling on fast IO path on objectstore side if possible. These are major requirements of constraint relaxation which can bring storage IO performance close to a bare hard drive bandwidths keeping strong consistency. In the next chapter I will cover all the requirements and will show how it can be implemented in practice. Separation of management requests from IO requests -------------------------------------------------- Requests in a storage cluster can be divided into two parts: management requests, which are object creation/deletion/listing, attributes setting/getting, lock management, etc; and IO requests, which do read or modify content of an object. The most prevailing requests are IO read and write requests, whose latency affect the overall performance of the entire cluster. The proposed requests separation has also a clear separation on metadata and data, which should be treated differently on the objectstore side, namely no journal should be involved in data modification (this will be discussed in detail below). Separating IO from management requests makes it possible to have a different primary replicas in a group of replicas (PG). For example in order to distribute a read load, primary can be chosen by an object id using persistent hashing (e.g. hash ring as proposed here [8]) instead of having a single primary. Write requests require replication involved and as was mentioned earlier client-driven replication model can be chosen in order to minimize latencies of an extra hop. Having different paths for several types of requests can be summarised as the following: 1. Management requests go through a persistent primary replica and log-based replication keeps strict ordering of operations in the entire group (PG). There are no changes to original Ceph architecture are proposed. 2. Read object requests are sent to different primaries according to some persistent hashing rule [8]. 3. Write requests can follow two IO paths, where on one of the paths client is fully responsible for request replication and delivers requests to all replicas in a group (PG). This path is always taken when group of replicas (PG) is in active and healthy state. The second IO path always lies through a primary, thus all requests are equally ordered (like it is right now implemented in Ceph). This path is taken in order to simplify all corner cases when a group of replicas (PG) is not in an active state and performs resynchronization process. Hybrid client-driven replication -------------------------------- Client-driven replication is quite self-descriptive: client is responsible for data delivery to replicas in a group (PG) avoiding one extra network hop, which exists in a primary-copy model. If client communicates with replicas in a group (PG) directly there is no longer the central point of synchronization, thus the one possible issue that needs to be considered is the possibility of write requests from several clients to come to replicas in different order: Client 1 Client 2 -------- -------- sends A to replica1 sends B to replica1 sends A to replica2 sends B to replica2 Replica 1 Replica 2 --------- --------- A B B A ** replica1 writes B to the disk and then overwrites B with A replica2 writes A to the disk and then overwrites A with B In this example both clients access the same block of the same object, but write requests become reordered differently because of the network. If this happened, then replicas can contain different data and they are out of sync. This issue has very bad consequences: future reads are undefined, especially if one of replicas crashed and clients start reading from another. This is definitely a data corruption. In order to solve the issue the order of writes to the same offset should be synchronized between replicas. Synchronization can be performed by marking each request with a timestamp, taking into consideration, that time flows equally on clients. Sub-microsecond time synchronization is not a problem [9], especially when there is the central point for all members - distributed state machine cluster, namely cluster of monitors in terms of Ceph. Having time synchronized, client marks write requests with a timestamp. According to the Thomas Write Rule [10] outdated requests are discarded by timestamp-based concurrency control keeping the correct order. However, in the case of the client-driven replication it is impossible to discard a change on all replicas simultaneously. Instead outdated request should be repeated for all replicas in a group (PG), here is an example: Client 1 Client 2 -------- -------- marks A with stamp 1 marks B with stamp 2 sends A to replica1 sends B to replica1 sends A to replica2 sends B to replica2 Replica 1 Replica 2 --------- -------------- -- on disk -- A B -- on disk -- B A ** replica1 writes B to the disk and rejects A with RETRY replica2 writes A to the disk and then overwrites A with B Request A is not written by replica1, because it is older then B request, which has been applied recently. Instead replica1 replied on A request with the RETRY error, which forces the client to mark request A with a new timestamp and resend it again to the whole group of replicas (PG): Client 1 -------- marks A with stamp 3 sends A to replica1 sends A to replica2 Replica 1 Replica 2 --------- --------- A A -- on disk -- B -- on disk -- B A ** replica1 overwrites B with A replica2 overwrites B with A The next question rises: why do we need to repeat write operation on all replicas, but not only on those who have just replied with the error? Let's consider overlapping data (here I do not consider the reason why clients send overlapping write requests concurrently, obviously this is a bug on a client side, but nevertheless we keep replicas in sync): Client 1 marks AAAA with stamp 1 Client 2 marks BBB with stamp 2 Client 3 marks C with stamp 3 Replica 1 Replica 2 --------- --------- C 3 BBB 2 C 3 AAAAA 1 AAAAA 1 --------- --------- below the line is the state on disk ABCBA AACAA ** replica1 writes AAAA, then overwrites the middle with BBB and then with C replica2 writes AAAA and overwrites the middle with C; BBB request is rejected Client2 receives the RETRY error and has to repeat BBB: Client 2 marks BBB with stamp 4 Replica 1 Replica 2 --------- --------- BBB 4 BBB 4 -- on disk -- C 3 -- on disk -- BBB 2 C 3 AAAAA 1 AAAAA 1 -------------- -------------- below the line is the state on disk ABBBA ABBBA On a second retry blocks on replicas become synchronized and write request is considered as completed and persistent on a drive. Since concurrent writes should never happen on well written client software (I expect distributed filesystems use lock primitives in order to prevent concurrent access) I do not expect any performance degradation because of frequent write retries, so current algorithm acts as a protection which keeps replicas in sync. I would like to emphasize several points obtained from the description of the algorithm described above: 1. Time synchronization on clients does not need to be very accurate, even hundreds of millisecond accuracy is enough. Described time-based algorithm does not depend on actual physical time, also it does not depend on real (absolute) order of writes sent by concurrent clients. Instead algorithm forces distributed cluster members to have a single view on requests order, which is enough to decide should request be rejected or executed. 2. Hybrid client-driven replication does not involve clients into data recovery process, thus leaving this job to replicas (exactly as it is right now). Here I describe the fast IO path only when group of replicas (PG) is in active and fully synchronized state. When group of replicas (PG) is in resynchronization state or client sends management type of requests (not IO), then for the sake of simplicity all communication goes through the main primary replica, as it is right now implemented in Ceph. Crash of a client in the middle of a replication ------------------------------------------------ Client-driven replication in contrast with a primary-copy model has another major issue which has to be considered in detail: crash of a client in the middle of a replication, that is, when client sends write request to one of replicas in a group (PG) and then crashes, leaving other replicas out of sync. Since there is no central point which controls the whole replication process, group of replicas have no information, was the write request confirmed by a whole group or something has happened to a client and special action should be performed. The issue can be solved by immediate confirmation of a successfully completed write request sent by non-primary replicas to a primary one. As was mentioned earlier, each object has its own primary which serves reads, thus write confirmations of a modified object are sent to a particular primary, which accounts number of confirmations for the modified object. If the confirmation did not come from all replicas in a group, then after the timeout elapsed a primary can start synchronizing an object on its own. In order to be sure that confirmations are accounted correctly, each of them is marked with a timestamp taken from a source write request, thus reordered and outdated confirmations can be discarded. I would like to stress the point, that accounting of confirmations happens in RAM only at runtime and does not involve any disk operations. If one of replicas crashes after a client crash then the whole group of replicas (PG) changes its state, further write requests take another path and forwarded to the main primary in a group and objects synchronization would be started anyway. The READ RECENT rule in action ------------------------------ In client-driven replication the order in which replicas receive requests and update blocks is out of control, thus readers can receive inconsistent data (also called weak consistency model [8]). To ensure that a once read block will never return to its previous value (READ RECENT rule mentioned earlier) all attempts to read non consistent (not confirmed from all replicas) block has to be delayed or rejected with an explicit error. Consider this scenario: 1. Client1 fans out write requests to all replicas. 2. Write request reaches only the primary one, client1 crashes. 3. Concurrent client2 reads the same block, since primary replica has applied write request from client1, client2 reads the requested block successfully and observes the change made by client1. 4. Primary replica crashes and new primary is selected, resync process in a group (PG) is started. 5. Concurrent client2 reads again the same block from a new primary, but since no one else observes the change written to previous primary made by client1, client2 receives old data. This scenario breaks the READ RECENT rule and introduces data corruption. In order to guarantee that block is consistent, read from line 3. should be delayed or rejected till all replicas confirm, that block is persistent. This can be achieved by the same confirmation mechanism described earlier: block is treated as inconsistent if it was modified and not all confirmations are received. I do not expect any performance degradation for generic read loads and can quote Sage here: “Reads in shared read/write workloads are usually unaffected by write operations. However, reads of uncommitted data can be delayed until the update commits. This increases read latency in certain cases, but maintains a fully consistent behavior for concurrent read and write operations in the event that all OSDs in the placement group simultaneously fail and the write ack is not delivered” [7]. Another issue, which has to be investigated in detail, is a full outage of a group of replicas (PG). When replicas are come back some of the objects can be out of sync, and without versioned log there is no any possibility to distinguish what blocks were updated recently and thus have a higher timestamp. As was mentioned earlier READ RECENT rule has one property: if block has not been read since the modification, then it is irrelevant what version of a block can be taken as a master copy. Thus, if read of an inconsistent block is delayed till it is persistent on all replicas, any replica in a group may be a “donor” of this particular block. From the statement above it follows that non-atomic or partial writes can happen. Indeed, from a filesystem or an application which acts as a cluster client, this requires special handling of a write failure, such as log replay, for dealing with inconsistencies left behind. But it will assume that each block on all replicas in a group (PG) has just one value, so in this way group remains synchronized and future reads won’t report different content in case of a change of the primary replica which serves reads. Journal-less objectstore for IO path ------------------------------------ Write atomicity requires certain support from an underlying hardware or software. Different techniques like journals, COW or soft-updates can be used in order to guarantee atomicity, but for no doubts this feature comes with a high price of the IO performance degradation. Since each modern filesystem or a DBMS application itself can take care of data inconsistency, atomicity constraint for the objectstore can be relaxed. In order to provide performance close to a bare hard drive, each replica has to submit writes to an underlying hardware immediately and without any requirements for request ordering or data atomicity. As was mentioned earlier, the reading delay mechanism of non-persistent blocks makes synchronization of objects after a complete shutdown possible without knowing where is the recent update. However, full synchronization can take days if an object is big enough. The problem of long resync can be solved by bookkeeping a bitmap of a possibly-out-of-sync blocks of an object. The algorithm can be described in just one sentence: “Bits are set before a write commences, and are cleared when there have been no writes for a while.” [11]. In highly distributed storage solution where a group of replicas (PG) can consist of different replicas during the whole life of a cluster and members in a group constantly changing, it is important not only to track out-of-sync blocks of an object, but also to keep versions of such changes, so that to answer the following question with minimal computational cost: what has changed in the object between versions N and M? Having versions of block changes in mind, one important modification to bitmap algorithm can be proposed: bitmap of out-of-sync blocks is stored in a file with a timestamp of a first write request in the name; after a certain number of changed blocks each replica does a rotation of the bitmap file, so that new bitmap file is created with a timestamp of a first write request which initiated the bitmap rotation process. The major difference to what RAID 1 does is, that bits are never cleared, but new bitmap file is created instead. Bits of modified but not yet persistent blocks (not all confirmations are received from replicas) migrate to a new bitmap file, in order to guarantee that block synchronization will take place in the future in case of possible replica failure. As timestamps of client write requests always change forward and the time on clients is synchronized (major requirement of proposed hybrid client-driven replication model), each replica will have similar view of block changes, so the question about data difference between the two versions can be easily answered. Proposed rotation of bitmap files can occupy extra disk space on a replica, especially when new files are created and never deleted. Deletion of bitmap files of old versions is not a problem: N files can be kept, rotation process spawns new bitmap file with a recent timestamp, meanwhile a file with the oldest timestamp is removed. If for some reason there will be a need to restore changes of an object version, which bitmap file was removed, then full object resynchronization should be performed. Summary ------- Proposed changes should relax a lot of constraints on fast IO path in current Ceph implementation keeping strong data consistency between replicas and bring performance of a single client to almost bare hard drive bandwidths. Even if there are logical holes in the model described above (there are), I still believe that eliminating them will not be impossible. PS. And yes, because of the “write hole” problem [12] erasure coding replication won’t survive full group outage without atomicity guarantees on the objectstore side. Sorry for that. -- Roman [1] R. Golding, Weak-consistency group communication and membership, PhD thesis, University of California, Santa Cruz, 1992. [2] Petersen et al., “Flexible Update Propagation for weakly consistent replication”, Proc. of the 16th ACM Symposium on Operating Systems Principles (SOSP), 1997. [3] M. Rabinovich. N. Gehani. A. Kononov, “Scalable Update Propagation in Epidemic Replicated Databases”,Advances in Database Technology - EDBT'96, Lecture Notes in Computer Science Vol. 1057, Springer, pp. 207-222. [4] G. Wuu, A Bernstein. „Efficient Solutions to the Replicated Log and Dictionary Problems”, Proceedings of the third ACM Symposium on Principles of Distributed Computing, August 1984, pp. 233-242. [6] https://en.wikipedia.org/wiki/Strong_consistency [7] Sage A. Weil. Ceph: Reliable, Scalable, And High-Performance Distributed Storage, PhD thesis, University of California, Santa Cruz, 2007. [8] Jiayuan Zhang, Yongwei Wu†, Yeh-Ching Chung. PROAR: A Weak Consistency Model For Ceph [9] Precision Time Protocol (PTP/IEEE-1588) [10] R. H. Thomas, “A majority consensus approach to concurrency control for multiple copy databases,” ACM Trans. Database Syst., vol. 4, no. 2, pp. 180–209, 1979. [11] Cluster support for MD/RAID 1, https://lwn.net/Articles/674085/ [12] https://en.wikipedia.org/wiki/RAID

3 years, 1 month

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v14.2.7 Nautilus released

by David Galloway

This is the seventh update to the Ceph Nautilus release series. This is a hotfix release primarily fixing a couple of security issues. We recommend that all users upgrade to this release. Notable Changes --------------- * CVE-2020-1699: Fixed a path traversal flaw in Ceph dashboard that could allow for potential information disclosure (Ernesto Puerta) * CVE-2020-1700: Fixed a flaw in RGW beast frontend that could lead to denial of service from an unauthenticated client (Or Friedmann) -- David Galloway Systems Administrator, RDU Ceph Engineering IRC: dgalloway

3 years, 1 month

1
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how to use the SSH orchestrator ?

by Milind Changire

I'm trying to get the SSH orchestrator running for testing the MDS Autoscaler <https://github.com/ceph/ceph/pull/32731>. I start the vstart cluster with: $ MDS=3 ../src/vstart.sh -d -b -l -n --without-dashboard --cephadm Processes seem to launch without errors until: ... /home/mchangir/work/mchangir-ceph.git/build/bin/ceph -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf -k /home/mchangir/work/mchangir-ceph.git/build/keyring fs volume create a Error EINVAL: Remote method threw exception: TypeError: %d format: a number is required, not NoneType Still digging at it. But no leads so far. Let me know where should I be looking for this one. The mgr plugin and related utilities python code looks okay to me. But I'm not 100% sure. ----- Will this failure get in my way for testing the MDS Autoscaler ? Also, `ceph status` shows the system in HEALTH_WARN state like so: $ ./bin/ceph status 2020-01-28T18:03:48.542+0530 7f4ddb872700 -1 WARNING: all dangerous and experimental features are enabled. 2020-01-28T18:03:48.558+0530 7f4dda610700 -1 WARNING: all dangerous and experimental features are enabled. cluster: id: e717ee71-d1e3-4be4-b771-1fface003e13 health: HEALTH_WARN 1 stray host(s) with 10 service(s) not managed by cephadm 10 stray service(s) not managed by cephadm services: mon: 3 daemons, quorum a,b,c (age 2h) mgr: x(active, since 2h) mds: a:1 {0=a=up:active} 2 up:standby osd: 3 osds: 3 up (since 2h), 3 in (since 2h) data: pools: 2 pools, 64 pgs objects: 22 objects, 2.2 KiB usage: 6.0 GiB used, 297 GiB / 303 GiB avail pgs: 64 active+clean ----- Also, Is this the correct way to remove an mds via the orchestrator: $ ./bin/ceph orchestrator mds rm c Error ENOENT: Unable to find mds.c[-*] daemon(s) but the daemons are indeed running: $ pgrep -a ceph- 1310763 /usr/libexec/platform-python -s /usr/bin/ceph-crash -n client.crash.localhost.localdomain 1624818 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mon -i a -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1624861 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mon -i b -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1624904 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mon -i c -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1626022 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-osd -i 0 -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1626376 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-osd -i 1 -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1626707 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-osd -i 2 -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1626895 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mds -i a -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1626950 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mds -i b -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1627005 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mds -i c -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf 1676260 /home/mchangir/work/mchangir-ceph.git/build/bin/ceph-mgr -i x -c /home/mchangir/work/mchangir-ceph.git/build/ceph.conf ----- Is the exception (in red) caused because of a missing placement spec ? Ideally I'd like to launch a standby MDS without any affinity to a filesystem or an OSD. Any help to get this correctly implemented/tested will be appreciated. -- Milind

3 years, 1 month

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Luminous 12.2.13 QE Validation status

by Yuri Weinstein

Details of this release summarized here: https://tracker.ceph.com/issues/42377#note-2 (This is a work in progress and some tests are still in queue, I on PTO starting next Thursday and trying to start review/approval/fix process early) rados - Neha approve? rgw - Casey approve ? rbd - Jason approve? krbd - Ilya approve? fs - Patrick approve? kcephfs - Patrick approve? multimds - still running ceph-deploy - Sage approve? ceph-disk - Nathan was looking? upgrade/client-upgrade-hammer (luminous) - Sage approve? upgrade/client-upgrade-jewel (luminous) - Sage approve? upgrade/luminous-p2p - Sage approve? upgrade/jewel-x (luminous) - Sage approve? upgrade/kraken-x (luminous) - REMOVED/ N/A powercycle - still running ceph-ansible - Bred FYI and ? upgrade/luminous-x (mimic) PASSED upgrade/luminous-x (nautilus) - one job rerunning, otherwise PASSED ceph-volume - PASSED, thx Jan for the fix (please speak up if something is missing) PS: We'd like to release it this week. Thx YuriW

3 years, 1 month

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rgw: bucket granularity sync

by Yehuda Sadeh-Weinraub

The following PR implements bucket granularity sync. We are aiming this feature to land in time for Octopus. https://github.com/ceph/ceph/pull/31686 Bucket granularity sync provides fine grained control of data movement between buckets in different zones. It extends the existing zone sync mechanism. In its core the feature modified the way the rgw sync process treats buckets. Previously buckets were being treated symmetrically, that is -- each (data) zone holds a mirror of that bucket that should be the same as all the other zones. Whereas now it is possible for buckets to diverge, and a bucket can pull data from other buckets (ones that don't share its name or its ID) in different zone. The sync process was assuming therefore that the bucket sync source and the bucket sync destination were always referring to the same bucket, now that is not the case anymore. A new sync policy that can supersede the old zonegroup coarse configuration (sync_from*) was implemented. The sync policy can be configured at the zonegroup level (and if it is configured it replaces the old style config), but it can also be configured at the bucket level. In the new sync policy we can define multiple groups that can contain lists of data-flow configurations, and lists of pipe configurations. The data-flow define the flow of data between the different zones. It can define symmetrical data flow, in which multiple zones sync data from each other, and it can define directional data flow, in which the data moves in one way from one zone to another. A pipe defines the actual buckets that can use these data flow, and the properties that are associated with it (for example: source object prefix). A sync policy group can be in 3 states: enabled: sync is allowed and enabled allowed: sync is allowed forbidden: sync (as defined by this group) is not allowed and can override other groups. A policy can be defined at the bucket level. A bucket level sync policy inherits the data flow of the zonegroup policy, and can only define a subset of what the zonegroup allows. A wildcard zone, and a wildcard bucket parameter in the policy defines all relevant zones, or all relevant buckets. In the context of a bucket policy it means the current bucket instance. A disaster recovery configuration where entire zones are mirrored doesn't require configuring anything on the buckets. However, for a fine grained bucket sync it would be better to configure the pipes to be synced by allowing (status=allowed) them at the zonegroup level (e.g., using wildcards), but only enable the specific sync at the bucket leve (status=enabled)l. If needed, the policy at the bucket level can limit the data movement to specific relevant zones. Any changes to the zonegroup policy will need to be applied on the zonegroup master zone, and require period update and commit. Changes to the bucket policy will need to be applied on the zonegroup master zone. The changes are dynamically handled by rgw. New radosgw-admin commands to control this feature were added: sync policy get sync group <create | modify | get | remove> sync group flow <create | remove> sync group pipe <create | remove> sync info Most are self explanatory. The notable one is sync info, which provides info about the expected sources and targets of the sync process at the current zone (or of another, effective zone), either at the zone level, or at the bucket level. Since a bucket can now define a policy that defines data movement from it towards a different bucket at a different zone, when the policy is created we also generate a list of bucket dependencies that are used as hints when a sync of any particular bucket happens. The fact that a bucket reference another bucket doesn't mean it actually sync to/from it, as the data flow might not permit it. Bucket sync can also be limited to specific source object prefixes. The S3 bucket replication api has also been implemented, and allows users to create replication rules between different buckets. Note though that while the AWS replication feature allows bucket replication within the same zone, rgw does not allow it at the moment. However, the rgw api also added a new 'Zone' array that allows users to select to what zones the specific bucket will be synced to. Following are some usage examples: The system in these examples includes 3 zones: us-east (the master zone), us-west, us-west-2. * Example 1: Two zones, complete mirror: This is similar to current sync capabilities, but being done via the new sync policy engine. Note that changes to the zonegroup sync policy require a period update and commit. [us-east] $ radosgw-admin sync group create --group-id=group1 --status=allowed [us-east] $ radosgw-admin sync group flow create --group-id=group1 \ --flow-id=flow-mirror --flow-type=symmetrical \ --zones=us-east,us-west [us-east] $ radosgw-admin sync group pipe create --group-id=group1 \ --pipe-id=pipe1 --source-zones='*' \ --source-bucket='*' --dest-zones='*' \ --dest-bucket='*' [us-east] $ radosgw-admin sync group modify --group-id=group1 --status=enabled [us-east] $ radosgw-admin period update --commit $ radosgw-admin sync info --bucket=buck { "sources": [ { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-east", "bucket": "buck:115b12b3-....4409.1" }, "params": { ... } } ], "dests": [ { "id": "pipe1", "source": { "zone": "us-east", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, ... } ], ... } } Note that the "id" field in the output above reflects the pipe rule that generated that entry, a single rule can generate multiple sync entries as can be seen in the example. [us-west] $ radosgw-admin sync info --bucket=buck { "sources": [ { "id": "pipe1", "source": { "zone": "us-east", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, ... } ], "dests": [ { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-east", "bucket": "buck:115b12b3-....4409.1" }, ... } ], ... } * Example 2: Directional entire zone backup Also similar to current sync capabilities. In here we add a third zone, us-west-2 that will be a replica of us-west, but data will not be replicated back from it. [us-east] $ radosgw-admin sync group flow create --group-id=group1 \ --flow-id=us-west-backup --flow-type=directional \ --source-zone=us-west --dest-zone=us-west-2 [us-east] $ radosgw-admin period update --commit Note that us-west has two dests: [us-west] $ radosgw-admin sync info --bucket=buck { "sources": [ { "id": "pipe1", "source": { "zone": "us-east", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, ... } ], "dests": [ { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-east", "bucket": "buck:115b12b3-....4409.1" }, ... }, { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-west-2", "bucket": "buck:115b12b3-....4409.1" }, ... } ], ... } Whereas us-west-2 has only source and no destinations: [us-west-2] $ radosgw-admin sync info --bucket=buck { "sources": [ { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck:115b12b3-....4409.1" }, "dest": { "zone": "us-west-2", "bucket": "buck:115b12b3-....4409.1" }, ... } ], "dests": [], ... } * Example 3: Mirror a specific bucket Using the same group configuration, but this time switching it to 'allowed' state, which means that sync is allowed but not enabled. [us-east] $ radosgw-admin sync group modify --group-id=group1 --status=allowed [us-east] $ radosgw-admin period update --commit And we will create a bucket level policy rule for existing bucket buck2. Note that the bucket needs to exist before being able to set this policy, and admin commands that modify bucket policies need to run on the master zone, however, they do not require period update. There is no need to change the data flow, as it is inherited from the zonegroup policy. A bucket policy flow will only be a subset of the flow defined in the zonegroup policy. Same goes for pipes, although a bucket policy can enable pipes that are not enabled (albeit not forbidden) at the zonegroup policy. [us-east] $ radosgw-admin sync group create --bucket=buck2 \ --group-id=buck2-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck2 \ --group-id=buck2-default --pipe-id=pipe1 \ --source-zones='*' --dest-zones='*' * Example 4: Limit bucket sync to specific zones: This will only sync buck3 to us-east (from any zone that flow allows to sync into us-east). [us-east] $ radosgw-admin sync group create --bucket=buck3 \ --group-id=buck3-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck3 \ --group-id=buck3-default --pipe-id=pipe1 \ --source-zones='*' --dest-zones=us-east * Example 5: sync from a different bucket Note that bucket sync only works (currently) across zones and not within the same zone. Set buck4 to pull data from buck5 [us-east] $ radosgw-admin sync group create --bucket=buck4 ' --group-id=buck4-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck4 \ --group-id=buck4-default --pipe-id=pipe1 \ --source-zones='*' --source-bucket=buck5 \ --dest-zones='*' can also limit it to specific zones, for example the following will only sync data originated in us-west: [us-east] $ radosgw-admin sync group pipe modify --bucket=buck4 \ --group-id=buck4-default --pipe-id=pipe1 \ --source-zones=us-west --source-bucket=buck5 \ --dest-zones='*' Checking the sync info for buck5 on us-west is interesting: [us-west] $ radosgw-admin sync info --bucket=buck5 { "sources": [], "dests": [], "hints": { "sources": [], "dests": [ "buck4:115b12b3-....14433.2" ] }, "resolved-hints-1": { "sources": [], "dests": [ { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck5" }, "dest": { "zone": "us-east", "bucket": "buck4:115b12b3-....14433.2" }, ... }, { "id": "pipe1", "source": { "zone": "us-west", "bucket": "buck5" }, "dest": { "zone": "us-west-2", "bucket": "buck4:115b12b3-....14433.2" }, ... } ] }, "resolved-hints": { "sources": [], "dests": [] } } Note that there are resolved hints, which means that the bucket buck5 found about buck4 syncing from it indirectly, and not from its own policy (the policy for buck5 itself is empty). * Example 6: Sync to different bucket The same mechanism can work for configuring data to be synced to (vs. synced from as in the previous example). Note that internally data is still pulled from the source at the destination zone: Set buck6 to "push" data to buck5 [us-east] $ radosgw-admin sync group create --bucket=buck6 \ --group-id=buck6-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck6 \ --group-id=buck6-default --pipe-id=pipe1 \ --source-zones='*' --source-bucket='*' \ --dest-zones='*' --dest-bucket=buck5 A wildcard bucket name means the current bucket in the context of bucket sync policy. Combined with the configuration in Example 5, we can now write data to buck6 on us-east, data will sync to buck5 on us-west, and from there it will be distributed to buck4 on us-east, and on us-west-2. * Example 7: source filters Sync from buck8 to buck9, but only objects that start with 'foo/': [us-east] $ radosgw-admin sync group create --bucket=buck8 \ --group-id=buck8-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck8 \ --group-id=buck8-default --pipe-id=pipe-prefix \ --prefix=foo/ --source-zones='*' --dest-zones='*' \ --dest-bucket=buck9 Also sync from buck8 to buck9 any object that has the tags color=blue or color=red [us-east] $ radosgw-admin sync group pipe create --bucket=buck8 \ --group-id=buck8-default --pipe-id=pipe-tags \ --tags-add=color=blue,color=red --source-zones='*' \ --dest-zones='*' --dest-bucket=buck9 And we can check the expected sync in us-east (for example): [us-east] $ radosgw-admin sync info --bucket=buck8 { "sources": [], "dests": [ { "id": "pipe-prefix", "source": { "zone": "us-east", "bucket": "buck8:115b12b3-....14433.5" }, "dest": { "zone": "us-west", "bucket": "buck9" }, "params": { "source": { "filter": { "prefix": "foo/", "tags": [] } }, ... } }, { "id": "pipe-tags", "source": { "zone": "us-east", "bucket": "buck8:115b12b3-....14433.5" }, "dest": { "zone": "us-west", "bucket": "buck9" }, "params": { "source": { "filter": { "tags": [ { "key": "color", "value": "blue" }, { "key": "color", "value": "red" } ] } }, ... } } ], ... } Note that there aren't any sources, only two different destinations (one for each configuration). When the sync process happens it will select the relevant rule for each object it syncs. Prefixes and tags can be combined, in which object will need to have both in order to be synced. The priority param can also be passed, and it can be used to determine when there are multiple different rules that are matched (and have the same source and destination), to determine which of the rules to be used. * Example 8: destination params: storage class Storage class of the destination objects can be configured: [us-east] $ radosgw-admin sync group create --bucket=buck10 \ --group-id=buck10-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck10 \ --group-id=buck10-default \ --pipe-id=pipe-storage-class \ --source-zones='*' --dest-zones=us-west-2 \ --storage-class=CHEAP_AND_SLOW * Example 9: destination params: destination owner translation Set the destination objects owner as the destination bucket owner. This requires specifying the uid of the destination bucket: [us-east] $ radosgw-admin sync group create --bucket=buck11 \ --group-id=buck11-default --status=enabled [us-east] $ radosgw-admin sync group pipe create --bucket=buck11 \ --group-id=buck11-default --pipe-id=pipe-dest-owner \ --source-zones='*' --dest-zones='*' \ --dest-bucket=buck12 --dest-owner=joe * Example 10: destination params: user mode User mode makes sure that the user has permissions to both read the objects, and write to the destination bucket. This requires that the uid of the user (which in its context the operation executes) is specified. [us-east] $ radosgw-admin sync group pipe modify --bucket=buck11 \ --group-id=buck11-default --pipe-id=pipe-dest-owner \ --mode=user --uid=jenny Please let me know if you have any questions. This might be tweaked a little bit, and there are a couple of additions that I would like to make, but at the moment that's where things stand. Yehuda

3 years, 1 month

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Fwd: Luminous 12.2.13 QE Validation status

by Yuri Weinstein

---------- Forwarded message --------- From: Brad Hubbard <bhubbard(a)redhat.com> Date: Tue, Jan 28, 2020 at 1:30 AM Subject: Re: Luminous 12.2.13 QE Validation status To: Yuri Weinstein <yweinste(a)redhat.com> On Tue, Jan 28, 2020 at 12:49 AM Yuri Weinstein <yweinste(a)redhat.com> wrote: > > UPDATE: > > rados - Neha approve? > rgw - Casey approved > rbd - Jason approve? > krbd - Ilya approved > fs - Patrick approve? > kcephfs - Patrick approve? > multimds - Patrick approve? > ceph-deploy - Sage approve? > ceph-disk - Nathan was looking? > upgrade/client-upgrade-hammer (luminous) - Sage approve? > upgrade/client-upgrade-jewel (luminous) - Sage approve? > upgrade/luminous-p2p - Sage approve? > upgrade/jewel-x (luminous) - Sage approve? > upgrade/kraken-x (luminous) - REMOVED/ N/A > powercycle - still running > ceph-ansible - Bred FYI and ? This will require the following teuthology trackers to be resolved (I've submitted PRs for all of them). https://tracker.ceph.com/issues/43798 https://tracker.ceph.com/issues/43799 https://tracker.ceph.com/issues/43843 Note that this will allow the tests to run on Mira or OVH. Luminous CA test will never run on Smithi but then I don't imagine it would need to run too many more times against luminous. > upgrade/luminous-x (mimic) PASSED > upgrade/luminous-x (nautilus) - PASSED > ceph-volume - PASSED > > Dev leads, pls review/approve runs so we can release this. > > Thx > YuriW > -- Cheers, Brad

3 years, 1 month

1
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